Abstract

This paper describes the Machine Learning and Language Processing (MLLP) ASR systems for the 2015 IWSLT evaluation campaign, The English system is based on the combination of five different subsystems which consist of two types of Neural Networks architectures (Deep feed-forward and Convolutional), two types of activation functions (sigmoid and rectified linear) and two types of input features (fMLLR and FBANK). All subsystems perform a speaker adaptation step based on confidence measures the output of which is then combined with ROVER. This system achieves a Word Error Rate (WER) of 13.3% on the 2015 official IWSLT English test set